raw.dta<-read_csv("../data/FSA_data_competition_2020.csv", na = c("NA", ":", "unclassified", "unknown")) %>%
rename("ID" = "ID",
"date_added" = "Date Added",
"date_published" = "Date of Publishing",
"data_source" = "Data Source",
"source_type" = "Source Type",
"alert_type" = "Alert Type",
"raw_text_product" = "Raw Product Phrase",
"product_categoty" = "Product Category",
"product" = "Commodity / Product",
"origin_country" = "Country of Origin",
"origin_country_EU" = "Eu/non-EU Country of Origin",
"notified_country" = "Notified by",
"notified_country_EU" = "EU/non-EU Notifying Country",
"incident_title" = "Incident Title",
"hazard_description" = "Hazard Description", # can extract about ecoli fro here
"hazard_group" = "Hazard Group",
"summary" = "Summary",
"link" = "Link",
"food_feed_fcm" = "Food; Feed or FCM",
"manufacturer" = "Manufacturer",
"brand" = "Brand",
"organisation" = "Organisations",
"food_or_not" = "Is A Food Article" )
# basic tidy
data <- raw.dta %>%
select(-food_or_not, -incident_title) %>%
mutate(food_feed_fcm = ifelse(food_feed_fcm == 'FCM', 'fcm',
ifelse(food_feed_fcm =='Food', 'food', food_feed_fcm))) %>%
filter(food_feed_fcm != "fcm")
data %>% arrange(date_published) %>% vis_miss()
# new cols
data <- data %>%
# tidy up dates
mutate(date_added = dmy(date_added),
date_published = dmy(date_published)) %>%
mutate(date_added_year = year(date_added),
date_published_year = year(date_published)) %>%
mutate(date_published_month = ifelse(nchar(month(date_published)) == 2, month(date_published), paste0(0, month(date_published))),
# create year_month
date_published_year_month = paste0(date_published_year, "-", date_published_month),
#create year_quarter
date_published_quarter = ifelse(date_published_month %in% c("01", "02", "03"), "Q1",
ifelse(date_published_month %in% c("04", "05", "06"), "Q2",
ifelse(date_published_month %in% c("07", "08", "09"), "Q3",
ifelse(date_published_month %in% c("10", "11", "12"), "Q4", NA)))),
date_published_year_quarter = paste0(date_published_year, "-", date_published_quarter) ) %>%
# create incident ID
separate(ID, into= c("ID", "ID_incident"), sep= "-", remove=F) %>%
mutate(ID_incident = ifelse(is.na(ID_incident), ID, ID_incident))
data %>% count(date_added_year)
## # A tibble: 2 x 2
## date_added_year n
## <dbl> <int>
## 1 2019 21876
## 2 2020 10244
data %>% count(date_published_year)
## # A tibble: 6 x 2
## date_published_year n
## <dbl> <int>
## 1 2016 4306
## 2 2017 6497
## 3 2018 8228
## 4 2019 10149
## 5 2020 2937
## 6 NA 3
data %>% count(product) %>% arrange(-n)
## # A tibble: 3,490 x 2
## product n
## <chr> <int>
## 1 <NA> 1973
## 2 chicken 1951
## 3 bakery product 1935
## 4 beef 1681
## 5 meat product 995
## 6 pepper 816
## 7 food supplement 720
## 8 cheese 660
## 9 pork 644
## 10 sesame 540
## # … with 3,480 more rows
data %>% count(alert_type) %>% arrange(-n)
## # A tibble: 10 x 2
## alert_type n
## <chr> <int>
## 1 recall 14064
## 2 border rejection 5504
## 3 alert 3649
## 4 information for attention 2692
## 5 update 2082
## 6 information for follow-up 1941
## 7 outbreak 1863
## 8 warning 182
## 9 information 109
## 10 lookout 34
data %>% count(data_source) %>% arrange(-n)
## # A tibble: 43 x 2
## data_source n
## <chr> <int>
## 1 RASFF Portal 11906
## 2 CDPH Recalls (Canada) 5075
## 3 Food Poisoning Bulletin (US) 2133
## 4 Ministry of Health - Border Rejections (Japan) 1850
## 5 FoodSafetyNews.com 1495
## 6 FSA Alerts & Recalls (UK) 1427
## 7 MAPAQ (Canada) 1230
## 8 FDA Recalls (USA) 904
## 9 Product Recalls Website: Oulah (France) 896
## 10 AFSCA Recalls (Belgium) 614
## # … with 33 more rows
data %>% count(hazard_group) %>% arrange(-n)
## # A tibble: 35 x 2
## hazard_group n
## <chr> <int>
## 1 microbial contaminants (other) 5831
## 2 pathogenic micro-organisms 5588
## 3 allergens 5221
## 4 <NA> 3171
## 5 foreign bodies 1896
## 6 composition 1536
## 7 poor or insufficient controls 1192
## 8 pesticide residues 1149
## 9 heavy metals 869
## 10 Fraud 783
## # … with 25 more rows
data %>% count(origin_country) %>% arrange(-n)
## # A tibble: 151 x 2
## origin_country n
## <chr> <int>
## 1 Canada 6508
## 2 USA 4763
## 3 United Kingdom 1966
## 4 France 1823
## 5 Italy 1130
## 6 China 1040
## 7 Belgium 1018
## 8 Poland 885
## 9 Netherlands 840
## 10 Turkey 826
## # … with 141 more rows
ggplot(data, aes(x = data_source, fill = alert_type)) +
geom_bar()+
theme_minimal_hgrid(10, rel_small = 1)+
#facet_grid(~alert_type)+
#scale_fill_manual(values=pal)+
coord_flip()+
labs(fill = "title", y="") +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
# Hazard categoty overview by Year
dat1<-data %>%
filter(!is.na(hazard_group),
date_published_year_month != "NA-NA") %>%
group_by(hazard_group) %>%
filter(n()>300) %>%
ungroup %>%
select(hazard_group, date_published_year ) %>%
group_by(hazard_group, date_published_year) %>%
mutate(count = n())%>%
distinct()
pc <-ggplot(data = dat1, aes(x = date_published_year , y = count, group = hazard_group)) +
geom_line(aes(color = hazard_group, alpha = 1), size = 1) +
geom_point(aes(color = hazard_group, alpha = 1), size = 3) +
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(10, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE)
pc
# Hazard categoty overview by Month (all records)
dat2<-data %>%
filter(!is.na(hazard_group),
date_published_year_month != "NA-NA") %>%
group_by(hazard_group) %>%
filter(n()>500) %>%
ungroup %>%
select(hazard_group, date_published_year_month ) %>%
group_by(hazard_group, date_published_year_month) %>%
mutate(count = n())%>%
distinct()
pc <-ggplot(data = dat2, aes(x = date_published_year_month , y = count, group = hazard_group)) +
geom_line(aes(color = hazard_group, alpha = 1), size = 1) +
geom_point(aes(color = hazard_group, alpha = 1), size = 3) +
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pc
# Hazard categoty overview by Month (report multiple issue related to one event as one event)
dat3<-data %>%
filter(!is.na(hazard_group),
date_published_year_month != "NA-NA") %>%
group_by(hazard_group) %>%
filter(n()>500) %>%
ungroup %>%
select(hazard_group, date_published_year_month, ID_incident) %>% distinct() %>%
group_by(hazard_group, date_published_year_month) %>%
mutate(count = n())%>%
distinct()
pc3 <-ggplot(data = dat3, aes(x = date_published_year_month , y = count, group = hazard_group)) +
geom_line(aes(color = hazard_group, alpha = 1), size = 1) +
geom_point(aes(color = hazard_group, alpha = 1), size = 3) +
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pc3
# Main 3 categories, with smooth lines
dat4<-data %>%
filter(!is.na(hazard_group),
date_published_year_month != "NA-NA") %>%
group_by(hazard_group) %>%
#filter(n()>500) %>%
ungroup %>%
select(hazard_group, date_published_year_month, ID_incident) %>% distinct() %>%
group_by(hazard_group, date_published_year_month) %>%
filter(hazard_group %in% c("microbial contaminants (other)", "pathogenic micro-organisms", "allergens")) %>%
mutate(count = n())%>%
distinct()
pc4 <-ggplot(data = dat4, aes(x = date_published_year_month , y = count, group = hazard_group)) +
#geom_line(aes(color = hazard_group, alpha = 1), size = 1) +
geom_point(aes(color = hazard_group, alpha = 1), size = 3) +
geom_smooth(aes(x = date_published_year_month , y = count, color = hazard_group))+
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pc4
# + other categories smooth line
dat5<-data %>%
filter(!is.na(hazard_group),
date_published_year_month != "NA-NA") %>%
group_by(hazard_group) %>%
filter(n()>500) %>%
ungroup %>%
select(hazard_group, date_published_year_month, ID_incident) %>% distinct() %>%
group_by(hazard_group, date_published_year_month) %>%
#filter(hazard_group %in% c("microbial contaminants (other)", "pathogenic micro-organisms", "allergens")) %>%
mutate(count = n())%>%
distinct()
pc5 <-ggplot(data = dat5, aes(x = date_published_year_month , y = count, group = hazard_group)) +
#geom_line(aes(color = hazard_group, alpha = 1), size = 1) +
geom_point(aes(color = hazard_group, alpha = 1), size = 3) +
geom_smooth(aes(x = date_published_year_month , y = count, color = hazard_group))+
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pc5
Foreign bodies - e.g. exploring plastic pollution
foreign <- data %>%
filter(hazard_group == "foreign bodies") %>%
mutate(contaminant = ifelse(str_detect(hazard_description, "(?i)plastic"), "plastic",
ifelse(str_detect( raw_text_product, "(?i)plastic"), "plastic",
ifelse(str_detect(hazard_description, "(?i)polystyrene"), "plastic",
ifelse(str_detect(hazard_description, "(?i)film"), "plastic",
ifelse(str_detect(hazard_description, "(?i)nylon"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)rubber"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)conveyor belt"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)blue particles"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)white and blue"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)packaging tape"), "plastic",
ifelse(str_detect(hazard_description, "(?i)glass"), "glass",
ifelse(str_detect( raw_text_product, "(?i)glass"), "glass",
ifelse(str_detect(hazard_description, "(?i)metal"), "metal",
ifelse(str_detect( raw_text_product, "(?i)metal"), "metal",
ifelse(str_detect( raw_text_product, "(?i)blade"), "metal",
ifelse(str_detect( raw_text_product, "(?i)aluminum"), "metal",
ifelse(str_detect( raw_text_product, "(?i)iron"), "metal",
ifelse(str_detect( raw_text_product, "(?i)sharp"), "metal",
ifelse(str_detect( raw_text_product, "(?i)needle"), "metal",
ifelse(str_detect(hazard_description, "(?i)wires"), "metal",
ifelse(str_detect(hazard_description, "(?i)nails"), "metal",
ifelse(str_detect(summary, "(?i)foil"), "metal",
ifelse(str_detect(hazard_description, "(?i)insect"), "insects",
ifelse(str_detect(raw_text_product, "(?i)insect"), "insects",
ifelse(str_detect(hazard_description, "(?i)bone"), "bone",
ifelse(str_detect( raw_text_product, "(?i)bone"), "bone",
ifelse(str_detect(hazard_description, "(?i)wood"), "wood",
ifelse(str_detect(raw_text_product, "(?i)wood"), "wood",
ifelse(str_detect(raw_text_product, "(?i)paper"), "paper",
ifelse(str_detect(raw_text_product, "(?i)carton"), "paper",
ifelse(str_detect(raw_text_product, "(?i)cardboard"), "paper",
ifelse(str_detect(raw_text_product, "(?i)soil"), "stones or soil",
ifelse(str_detect(raw_text_product, "(?i)sand "), "stones or soil",
ifelse(str_detect(raw_text_product, "(?i)pebble"), "stones or soil",
ifelse(str_detect(raw_text_product, "(?i)stone"), "stones or soil",
ifelse(str_detect(raw_text_product, "(?i)gravel"), "stones or soil",
ifelse(str_detect(raw_text_product, "(?i)rock"), "stones or soil",
ifelse(str_detect(raw_text_product, "(?i)mice"), "rodents",
ifelse(str_detect(raw_text_product, "(?i)mouse"), "rodents",
ifelse(str_detect(raw_text_product, "(?i)rodent"), "rodents",
"other foreign body")))))))))))))))))))))))))))))))))))))))))
# all foreign bodies
dat7<-data %>%
filter(!is.na(hazard_group),
date_published_year_month != "NA-NA") %>%
group_by(hazard_group) %>%
filter(n()>500) %>%
ungroup %>%
select(hazard_group, date_published_year_month, ID_incident) %>% distinct() %>%
group_by(hazard_group, date_published_year_month) %>%
filter(hazard_group == "foreign bodies") %>%
mutate(count = n())%>%
distinct()
pc7 <-ggplot(data = dat7, aes(x = date_published_year_month , y = count, group = hazard_group)) +
#geom_line(aes(color = hazard_group, alpha = 1), size = 1) +
geom_point(aes(color = hazard_group, alpha = 1), size = 3) +
geom_smooth(aes(x = date_published_year_month , y = count, color = hazard_group))+
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pc7
# by contaminant group
dat7<-foreign %>%
filter(date_published_year_month != "NA-NA") %>%
group_by(contaminant) %>%
#filter(n()>500) %>%
ungroup %>%
select(contaminant, date_published_year_month, ID_incident) %>% distinct() %>%
group_by(contaminant, date_published_year_month) %>%
mutate(count = n())%>%
distinct()
pc7 <-ggplot(data = dat7, aes(x = date_published_year_month , y = count, group = contaminant)) +
#geom_line(aes(color = contaminant, alpha = 1), size = 1) +
geom_point(aes(color = contaminant, alpha = 1), size = 3) +
geom_smooth(aes(x = date_published_year_month , y = count, color = contaminant))+
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pc7
## try to get contaminant from other groups
foreign_extra <- data %>%
filter(hazard_group != "foreign bodies") %>%
mutate(contaminant = ifelse(str_detect(hazard_description, "(?i)plastic"), "plastic",
ifelse(str_detect( raw_text_product, "(?i)plastic"), "plastic",
ifelse(str_detect(hazard_description, "(?i)polystyrene"), "plastic",
ifelse(str_detect(hazard_description, "(?i)film"), "plastic",
ifelse(str_detect(hazard_description, "(?i)nylon"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)rubber"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)conveyor belt"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)blue particles"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)white and blue"), "plastic",
ifelse(str_detect(raw_text_product, "(?i)packaging tape"), "plastic",
ifelse(str_detect(hazard_description, "(?i)metal"), "metal",
ifelse(str_detect( raw_text_product, "(?i)metal"), "metal",
ifelse(str_detect( raw_text_product, "(?i)blade"), "metal",
ifelse(str_detect( raw_text_product, "(?i)aluminum"), "metal",
ifelse(str_detect( raw_text_product, "(?i) iron"), "metal",
ifelse(str_detect( raw_text_product, "(?i)sharp"), "metal",
ifelse(str_detect( raw_text_product, "(?i)needle"), "metal",
ifelse(str_detect(hazard_description, "(?i)wires"), "metal",
ifelse(str_detect(hazard_description, "(?i)nails"), "metal",
ifelse(str_detect(summary, "(?i)foil"), "metal",
ifelse(str_detect(hazard_description, "(?i)glass"), "glass",
ifelse(str_detect( raw_text_product, "(?i)glass"), "glass",
ifelse(str_detect(hazard_description, "(?i)insect"), "insects",
ifelse(str_detect(raw_text_product, "(?i)insect"), "insects",
"other foreign body"))))))))))))))))))))))))) %>%
filter(contaminant != "other foreign body") %>%
filter(!(contaminant == "metal" & grepl("heavy metal|metallic|Lead|alkaloid|metalaxyl", raw_text_product, ignore.case = T))) %>%
filter(!(contaminant == "metal" & grepl("heavy metal|metallic|Lead|alkaloid", summary, ignore.case = T))) %>%
filter(!(contaminant == "metal" & hazard_description %in% c("undeclared peanut", "unauthorised substance magnesium",
"unauthorised substance iron glycinate chelate", "too high content of iron",
"salmonella outbreak" ,"salmonella spp sticks", "copper", "undesignated additive"))) %>%
filter(!(contaminant %in% c("plastic", "glass") & !grepl("piece|foreign|extraneous|find|bits|particle|plastic in", raw_text_product, ignore.case = T)))
# add all contaminants
dat8<-bind_rows(foreign, foreign_extra) %>%
filter(date_published_year_month != "NA-NA") %>%
group_by(contaminant) %>%
#filter(n()>500) %>%
ungroup %>%
select(contaminant, date_published_year_month, ID_incident) %>% distinct() %>%
group_by(contaminant, date_published_year_month) %>%
mutate(count = n())%>%
distinct()
pc8 <-ggplot(data = dat8, aes(x = date_published_year_month , y = count, group = contaminant)) +
#geom_line(aes(color = contaminant, alpha = 1), size = 1) +
geom_point(aes(color = contaminant, alpha = 1), size = 3) +
geom_smooth(aes(x = date_published_year_month , y = count, color = contaminant))+
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pc8
# only 4 cataegories
dat9<-bind_rows(foreign, foreign_extra) %>%
filter(date_published_year_month != "NA-NA") %>%
filter(date_published_year_quarter != "2020-Q2") %>%
filter(contaminant %in% c("plastic", "metal", "glass", "insects")) %>%
group_by(contaminant) %>%
#filter(n()>500) %>%
ungroup %>%
select(contaminant, date_published_year_month, date_published_year, date_published_year_quarter, ID_incident) %>% distinct() %>%
group_by(contaminant, date_published_year_month, date_published_year, date_published_year_quarter) %>%
mutate(count = n())%>%
distinct()
pc9 <-ggplot(data = dat9, aes(x = date_published_year_month , y = count, group = contaminant)) +
#geom_line(aes(color = contaminant, alpha = 1), size = 1) +
geom_point(aes(color = contaminant, alpha = 1), size = 2) +
geom_smooth(aes(x = date_published_year_month , y = count, color = contaminant))+
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
pc10 <-ggplot(data = dat9, aes(x = date_published_year_quarter , y = count, group = contaminant)) +
#geom_line(aes(color = contaminant, alpha = 1), size = 1) +
geom_point(aes(color = contaminant, alpha = 1), size = 2) +
geom_smooth(aes(x = date_published_year_quarter , y = count, color = contaminant))+
#scale_x_continuous(breaks = sort(unique(dat$date_published_year))[c(TRUE, FALSE)] )+
theme(legend.position = "right", ncol=1) +
#scale_colour_manual(values=c(pal))+
theme_minimal_hgrid(9, rel_small = 1) +
labs(x = "year", colour="hazard group",
y = "counts",
title = "")+
guides(alpha = FALSE) +
theme(axis.text.x = element_text(angle = 45, hjust = 1))
foreign_all<-bind_rows(foreign, foreign_extra)
dim(foreign_all)
## [1] 2126 29
foreign_all %>% count(origin_country, sort=T)
## # A tibble: 78 x 2
## origin_country n
## <chr> <int>
## 1 Canada 542
## 2 USA 240
## 3 United Kingdom 216
## 4 Germany 166
## 5 France 156
## 6 Belgium 94
## 7 Netherlands 87
## 8 Italy 76
## 9 Denmark 64
## 10 Austria 38
## # … with 68 more rows
foreign_all %>% count(notified_country, sort=T)
## # A tibble: 36 x 2
## notified_country n
## <chr> <int>
## 1 Canada 532
## 2 United Kingdom 241
## 3 Germany 216
## 4 USA 200
## 5 France 141
## 6 Denmark 119
## 7 <NA> 98
## 8 Netherlands 83
## 9 Belgium 82
## 10 Italy 75
## # … with 26 more rows
Add coordinates data
add_coordinates <- function(input){
## get coordinates
# get world map
wmap <- getMap(resolution="high")
# get centroids
centroids <- gCentroid(wmap, byid=TRUE)
# get a data.frame with centroids
geo_df <- as.data.frame(centroids)
colnames(geo_df) <- c("long", "lat")
geo_df <- geo_df %>% tibble::rownames_to_column("country")
# update names in data
input<- input %>%
mutate( origin_country = case_when(origin_country == "USA" ~ "United States of America",
origin_country =="Hong Kong" ~ "Hong Kong S.A.R.",
origin_country =="Serbia" ~ "Republic of Serbia",
origin_country =="Bosnia Herzegovina" ~ "Bosnia and Herzegovina",
origin_country =="Tanzania" ~ "United Republic of Tanzania",
TRUE ~ origin_country)) %>%
mutate( notified_country = case_when(notified_country == "USA" ~ "United States of America",
notified_country =="Hong Kong" ~ "Hong Kong S.A.R.",
notified_country =="Serbia" ~ "Republic of Serbia",
notified_country =="Bosnia Herzegovina" ~ "Bosnia and Herzegovina",
notified_country =="Tanzania" ~ "United Republic of Tanzania",
TRUE ~ notified_country)) %>%
filter(!origin_country %in% c("Palestinian Territories", "INFOSAN" , "Commission Services", NA) | !notified_country %in% c("Palestinian Territories", "INFOSAN" , "Commission Services", NA))
# join with coords data
output <- input %>%
left_join(., geo_df, by = c("origin_country" = "country")) %>%
rename("lat.origin" = "lat",
"long.origin" = "long") %>%
left_join(., geo_df, by = c("notified_country" = "country")) %>%
rename("lat.notified" = "lat",
"long.notified" = "long") %>% drop_na()
return(output)
}
test <- add_coordinates(data)
foreign_all %>% count(alert_type, sort=T)
## # A tibble: 10 x 2
## alert_type n
## <chr> <int>
## 1 recall 1400
## 2 alert 389
## 3 information for follow-up 141
## 4 border rejection 80
## 5 information for attention 79
## 6 update 21
## 7 lookout 6
## 8 outbreak 6
## 9 information 3
## 10 warning 1
foreign_all %>% count(product_categoty, sort=T)
## # A tibble: 31 x 2
## product_categoty n
## <chr> <int>
## 1 fruits and vegetables 380
## 2 meat and meat products (other than poultry) 303
## 3 cereals and bakery products 261
## 4 poultry meat and poultry meat products 176
## 5 <NA> 175
## 6 milk and milk products 160
## 7 nuts; nut products and seeds 119
## 8 prepared dishes and snacks 91
## 9 cocoa and cocoa preparations; coffee and tea 79
## 10 soups; broths; sauces and condiments 71
## # … with 21 more rows
foreign_all %>% count(contaminant, sort=T)
## # A tibble: 10 x 2
## contaminant n
## <chr> <int>
## 1 metal 622
## 2 plastic 502
## 3 other foreign body 351
## 4 glass 307
## 5 insects 221
## 6 wood 37
## 7 rodents 35
## 8 bone 28
## 9 stones or soil 18
## 10 paper 5
foreign_all<-foreign_all %>%
mutate(contaminant2 = ifelse(contaminant %in% c("wood", "rodents", "bone","stones or soil", "paper"), "other foreign body", contaminant))
foreign_all %>%
select(-link, -brand, -manufacturer, -raw_text_product, -organisation, -date_added, -date_added_year) %>%
add_coordinates() %>%
write_tsv("../data/food_hazards_foreign_bodies.csv")
save all with coords
data %>%
select(-link, -brand, -manufacturer, -raw_text_product, -organisation, -date_added, -date_added_year) %>%
add_coordinates() %>%
write_tsv("../data/food_hazards_data_all.csv")